Pete Atkinson

Head of the School of Geography
University of Southampton, UK
August 2010

Pete Atkinson (B.Sc., Ph.D., FRGS, FRSS, FRSPS) visited UCSB for a brief period in August 2010 to work with UCSB researchers on the impacts of climate change in the Mediterannean region. While here Atkinson also collaborated with Prof. Michael Goodchild and Dr. Jingxiong Zhang (Wuhan University, China) on a book project (Scale in Geospatial Information and Analysis). The visit is funded by the EU-funded Marie Curie project CliMed, which UCSB participates in and which is led by Cukurova University in Turkey.

Atkinson has research interests in scale and scaling in remote sensing, particularly focused on downscaling and super–resolution mapping. He has also worked on flood forecasting using data assimilation approaches, and distributed sensor networks. He has growing interests in the modeling and prediction of disease including malaria in Kenya (with Dr. Pete Gething and others at Oxford University, UK), Sleeping Sickness in Uganda (with Prof. Sue Welburn and others at Edinburgh University, UK) and STIs in the UK as well as mapping maternal health in Ghana (with Prof. Zoe Matthews at Southampton University, UK). He also has interests in agent-based modeling of disease transmission systems (with Dr. Yong Yang of Michigan University). Pete Atkinsons interests in remote sensing and disease mapping (Curran et al., 2000) arose primarily from a research visit to Green College and the Department of Zoology, Oxford University.

Atkinson was Editor of the International Journal of Remote Sensing Letters from 2001 to 2009 and sits on the editorial board of several further journals including Geographical Analysis and Mathematical Geosciences. He has published around 120 journal articles, edited about eight books and authored or co-authored around 45 book chapters. He is particularly interested in collaborating in future on (i) multiple-point statistics for simulating and downscaling imagery, (ii) agent-based modeling of disease systems and (iii) remote sensing for global mapping of vegetation phenology.